


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 global f  # a global dp table for knapsack
 if f[i][j] < 0:
  if j < wt[i - 1]:
   _lowercase:       Any						=				mf_knapsack(i - 1 ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      )
  else:
   _lowercase:       Dict						=				max(
       mf_knapsack(i - 1 ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ) ,   mf_knapsack(i - 1 ,   __magic_name__ ,   __magic_name__ ,   j - wt[i - 1]      ) + val[i - 1] ,   )
  _lowercase:       str						=				val
 return f[i][j]


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 _lowercase:       Optional[int]						=				[[0] * (w + 1) for _ in range(n + 1      )]

 for i in range(1 ,   n + 1      ):
  for w_ in range(1 ,   w + 1      ):
   if wt[i - 1] <= w_:
    _lowercase:       str						=				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,   dp[i - 1][w_]      )
   else:
    _lowercase:       str						=				dp[i - 1][w_]

 return dp[n][w_], dp


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 if not (isinstance(__magic_name__ ,   (list, tuple)      ) and isinstance(__magic_name__ ,   (list, tuple)      )):
  raise ValueError(
      "Both the weights and values vectors must be either lists or tuples"      )

 _lowercase:       Optional[int]						=				len(__magic_name__      )
 if num_items != len(__magic_name__      ):
  _lowercase:       List[str]						=				(
      "The number of weights must be the same as the number of values.\n"
      f"But got {num_items} weights and {len(__magic_name__      )} values"
  )
  raise ValueError(__magic_name__      )
 for i in range(__magic_name__      ):
  if not isinstance(wt[i] ,   __magic_name__      ):
   _lowercase:       List[Any]						=				(
       "All weights must be integers but got weight of "
       f"type {type(wt[i]      )} at index {i}"
   )
   raise TypeError(__magic_name__      )

 _lowercase					,	_lowercase:       int						=				knapsack(__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      )
 _lowercase:       int						=				set()
 _construct_solution(__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      )

 return optimal_val, example_optional_set


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__ ,   __magic_name__      ):
 # for the current item i at a maximum weight j to be part of an optimal subset,
 # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
 # where i - 1 means considering only the previous items at the given maximum weight
 if i > 0 and j > 0:
  if dp[i - 1][j] == dp[i][j]:
   _construct_solution(__magic_name__ ,   __magic_name__ ,   i - 1 ,   __magic_name__ ,   __magic_name__      )
  else:
   optimal_set.add(__magic_name__      )
   _construct_solution(__magic_name__ ,   __magic_name__ ,   i - 1 ,   j - wt[i - 1] ,   __magic_name__      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			Optional[Any]        =							[3, 2, 4, 4]
       _SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							[4, 3, 2, 3]
       _SCREAMING_SNAKE_CASE     :			Optional[int]        =							4
       _SCREAMING_SNAKE_CASE     :			Optional[int]        =							6
       _SCREAMING_SNAKE_CASE     :			Tuple        =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
       _SCREAMING_SNAKE_CASE     :			str        =							knapsack(w, wt, val, n)
       print(optimal_solution)
       print(mf_knapsack(n, wt, val, w))  # switched the n and w

       # testing the dynamic programming problem with example
       # the optimal subset for the above example are items 3 and 4
       _SCREAMING_SNAKE_CASE     :			Dict        =							knapsack_with_example_solution(w, wt, val)
       assert optimal_solution == 8
       assert optimal_subset == {3, 4}
       print('optimal_value = ', optimal_solution)
       print('An optimal subset corresponding to the optimal value', optimal_subset)


"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
